Course evaluations (~15 mins)

https://canvas.ubc.ca/courses/170662/external_tools/53187

• They help us improve our teaching!
• UBC & CS uses them to provide rewards to instructors and TAs who are doing well!
• UBC & CS uses them to identify where instructors, TAs and courses need additional supports to improve.
• UBC uses these in evaluating professors for tenure and promotion.
• I’ll very much appreciate your constructive and concrete feedback.

What did we cover

• Part 1: Supervised learning on tabular data: ML fundamentals, preprocessing and data encoding, a bunch of models, evaluation metrics, feature importances and model transparency, feature selection, hyperparameter optimization

• Part 2: Dealing with other non-tabular data types: Clustering, recommender systems, computer vision with pre-trained deep learning models (high level), language data, text preprocessing, embeddings, topic modeling, time series, right-censored data / survival analysis

• Part 3: Communication, Ethics, and Deployment

What we didn’t cover

• How do these models work under the hood

What next?

If you want to further develop your machine learning skills:

• Practice!

• Work on your own projects. Make your work available and reproducible.

• If you are interested in research in machine learning

  • Take CPSC 340. If you do not have the required prereqs you can try to audit it.
  • Get into the habit of reading papers and replicating results

❓❓ Questions for you

For each of the scenarios below

• Identify if ML is a good solution for a problem. If yes
  • Frame the problem to a ML problem.
  • Discuss what kind of features you would need to effectively solve the problem
  • What would be a reasonable baseline?
  • Which model would be a suitable model for the given scenario?
  • What would be the appropriate success metrics.